Summary of On the Generalization Capacity Of Neural Networks During Generic Multimodal Reasoning, by Takuya Ito et al.
On the generalization capacity of neural networks during generic multimodal reasoning
by Takuya Ito, Soham Dan, Mattia Rigotti, James Kozloski, Murray Campbell
First submitted to arxiv on: 26 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper evaluates large language models’ ability to generalize to multimodal domains, testing their performance in various out-of-distribution scenarios. The authors introduce a new benchmark, Generic COG (gCOG), to assess this capability. They find that models with multiple attention layers or cross-attention mechanisms perform better across different architectures, including RNNs and Transformers. However, these models still struggle with productive generalization, indicating fundamental limitations for certain types of multimodal reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how well language models can handle new information from different sources, such as images or audio. It creates a special test to see which models are best at this task. The results show that some models do better than others because they have special attention mechanisms. However, even the top-performing models struggle with very complex tasks. |
Keywords
* Artificial intelligence * Attention * Cross attention * Generalization
